Overview

Dataset statistics

Number of variables14
Number of observations11925
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory120.0 B

Variable types

Text2
Numeric12

Alerts

percentage0to15years is highly overall correlated with percentagehouseholdswithchildrenHigh correlation
percentage25to45years is highly overall correlated with percentage45to65years and 4 other fieldsHigh correlation
percentage45to65years is highly overall correlated with percentage25to45yearsHigh correlation
percentage65yearsorolder is highly overall correlated with percentage25to45yearsHigh correlation
percentagehouseholdswithchildren is highly overall correlated with percentage0to15years and 1 other fieldsHigh correlation
percentagehouseholdswithoutchildren is highly overall correlated with percentage25to45years and 2 other fieldsHigh correlation
percentagenonwesternmigrationbackground is highly overall correlated with percentage25to45years and 4 other fieldsHigh correlation
percentageonepersonhouseholds is highly overall correlated with percentagehouseholdswithchildren and 3 other fieldsHigh correlation
percentagewesternmigrationbackground is highly overall correlated with percentagenonwesternmigrationbackground and 1 other fieldsHigh correlation
populationdensityperkm2 is highly overall correlated with percentage25to45years and 1 other fieldsHigh correlation
neighborhoodcode has unique valuesUnique
percentagenonwesternmigrationbackground has 1420 (11.9%) zerosZeros

Reproduction

Analysis started2024-07-05 09:48:53.055909
Analysis finished2024-07-05 09:49:17.491359
Duration24.44 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

neighborhoodcode
Text

UNIQUE 

Distinct11925
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size186.3 KiB
2024-07-05T11:49:17.658595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters119250
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11925 ?
Unique (%)100.0%

Sample

1st rowBU00030000
2nd rowBU00030001
3rd rowBU00030002
4th rowBU00030007
5th rowBU00030008
ValueCountFrequency (%)
bu00030000 1
 
< 0.1%
bu00050003 1
 
< 0.1%
bu00090102 1
 
< 0.1%
bu00090101 1
 
< 0.1%
bu00030002 1
 
< 0.1%
bu00030007 1
 
< 0.1%
bu00030008 1
 
< 0.1%
bu00030009 1
 
< 0.1%
bu00050000 1
 
< 0.1%
bu00050001 1
 
< 0.1%
Other values (11915) 11915
99.9%
2024-07-05T11:49:18.091827image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 35569
29.8%
1 13038
 
10.9%
B 11925
 
10.0%
U 11925
 
10.0%
2 7559
 
6.3%
3 7379
 
6.2%
4 5719
 
4.8%
9 5660
 
4.7%
5 5615
 
4.7%
7 5041
 
4.2%
Other values (2) 9820
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 95400
80.0%
Uppercase Letter 23850
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35569
37.3%
1 13038
 
13.7%
2 7559
 
7.9%
3 7379
 
7.7%
4 5719
 
6.0%
9 5660
 
5.9%
5 5615
 
5.9%
7 5041
 
5.3%
8 4933
 
5.2%
6 4887
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
B 11925
50.0%
U 11925
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 95400
80.0%
Latin 23850
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35569
37.3%
1 13038
 
13.7%
2 7559
 
7.9%
3 7379
 
7.7%
4 5719
 
6.0%
9 5660
 
5.9%
5 5615
 
5.9%
7 5041
 
5.3%
8 4933
 
5.2%
6 4887
 
5.1%
Latin
ValueCountFrequency (%)
B 11925
50.0%
U 11925
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35569
29.8%
1 13038
 
10.9%
B 11925
 
10.0%
U 11925
 
10.0%
2 7559
 
6.3%
3 7379
 
6.2%
4 5719
 
4.8%
9 5660
 
4.7%
5 5615
 
4.7%
7 5041
 
4.2%
Other values (2) 9820
 
8.2%
Distinct11058
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Memory size186.3 KiB
2024-07-05T11:49:18.407959image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length60
Median length50
Mean length15.725786
Min length2

Characters and Unicode

Total characters187530
Distinct characters81
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10622 ?
Unique (%)89.1%

Sample

1st rowAppingedam-Centrum
2nd rowAppingedam-West
3rd rowAppingedam-Oost
4th rowVerspreide huizen Damsterdiep en Eemskanaal
5th rowVerspreide huizen ten zuiden van Eemskanaal
ValueCountFrequency (%)
verspreide 1463
 
6.7%
huizen 1458
 
6.7%
de 765
 
3.5%
en 602
 
2.7%
buitengebied 319
 
1.5%
omgeving 223
 
1.0%
kern 217
 
1.0%
noord 204
 
0.9%
180
 
0.8%
zuid 177
 
0.8%
Other values (9254) 16297
74.4%
2024-07-05T11:49:18.957273image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 30196
16.1%
r 14755
 
7.9%
n 12789
 
6.8%
i 11340
 
6.0%
9980
 
5.3%
o 9316
 
5.0%
d 8582
 
4.6%
t 8431
 
4.5%
u 8204
 
4.4%
s 7712
 
4.1%
Other values (71) 66225
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 154447
82.4%
Uppercase Letter 19695
 
10.5%
Space Separator 9980
 
5.3%
Dash Punctuation 2124
 
1.1%
Other Punctuation 584
 
0.3%
Decimal Number 479
 
0.3%
Open Punctuation 109
 
0.1%
Close Punctuation 109
 
0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 30196
19.6%
r 14755
9.6%
n 12789
 
8.3%
i 11340
 
7.3%
o 9316
 
6.0%
d 8582
 
5.6%
t 8431
 
5.5%
u 8204
 
5.3%
s 7712
 
5.0%
a 7445
 
4.8%
Other values (25) 35677
23.1%
Uppercase Letter
ValueCountFrequency (%)
V 2241
 
11.4%
B 2032
 
10.3%
H 1399
 
7.1%
D 1372
 
7.0%
W 1288
 
6.5%
O 1198
 
6.1%
S 1168
 
5.9%
N 1115
 
5.7%
Z 1033
 
5.2%
K 980
 
5.0%
Other values (15) 5869
29.8%
Decimal Number
ValueCountFrequency (%)
1 113
23.6%
0 96
20.0%
2 94
19.6%
3 88
18.4%
4 34
 
7.1%
5 23
 
4.8%
6 9
 
1.9%
9 8
 
1.7%
7 7
 
1.5%
8 7
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 260
44.5%
' 133
22.8%
, 110
18.8%
/ 73
 
12.5%
" 6
 
1.0%
& 2
 
0.3%
Space Separator
ValueCountFrequency (%)
9980
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2124
100.0%
Open Punctuation
ValueCountFrequency (%)
( 109
100.0%
Close Punctuation
ValueCountFrequency (%)
) 109
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 174142
92.9%
Common 13388
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 30196
17.3%
r 14755
 
8.5%
n 12789
 
7.3%
i 11340
 
6.5%
o 9316
 
5.3%
d 8582
 
4.9%
t 8431
 
4.8%
u 8204
 
4.7%
s 7712
 
4.4%
a 7445
 
4.3%
Other values (50) 55372
31.8%
Common
ValueCountFrequency (%)
9980
74.5%
- 2124
 
15.9%
. 260
 
1.9%
' 133
 
1.0%
1 113
 
0.8%
, 110
 
0.8%
( 109
 
0.8%
) 109
 
0.8%
0 96
 
0.7%
2 94
 
0.7%
Other values (11) 260
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 187462
> 99.9%
None 68
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 30196
16.1%
r 14755
 
7.9%
n 12789
 
6.8%
i 11340
 
6.0%
9980
 
5.3%
o 9316
 
5.0%
d 8582
 
4.6%
t 8431
 
4.5%
u 8204
 
4.4%
s 7712
 
4.1%
Other values (62) 66157
35.3%
None
ValueCountFrequency (%)
ë 31
45.6%
â 17
25.0%
é 6
 
8.8%
û 5
 
7.4%
ö 4
 
5.9%
ï 2
 
2.9%
ô 1
 
1.5%
ú 1
 
1.5%
á 1
 
1.5%

populationdensityperkm2
Real number (ℝ)

HIGH CORRELATION 

Distinct5865
Distinct (%)49.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3255.7764
Minimum2
Maximum36484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size186.3 KiB
2024-07-05T11:49:19.173761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile18
Q1153
median1920
Q35108
95-th percentile10283.8
Maximum36484
Range36482
Interquartile range (IQR)4955

Descriptive statistics

Standard deviation4021.9154
Coefficient of variation (CV)1.2353168
Kurtosis8.2743042
Mean3255.7764
Median Absolute Deviation (MAD)1870
Skewness2.2868234
Sum38825134
Variance16175803
MonotonicityNot monotonic
2024-07-05T11:49:19.357302image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 57
 
0.5%
18 56
 
0.5%
15 55
 
0.5%
14 53
 
0.4%
12 50
 
0.4%
13 50
 
0.4%
33 50
 
0.4%
21 49
 
0.4%
32 48
 
0.4%
31 45
 
0.4%
Other values (5855) 11412
95.7%
ValueCountFrequency (%)
2 4
 
< 0.1%
3 7
 
0.1%
4 9
 
0.1%
5 20
0.2%
6 16
 
0.1%
7 26
0.2%
8 38
0.3%
9 41
0.3%
10 43
0.4%
11 36
0.3%
ValueCountFrequency (%)
36484 1
< 0.1%
35903 1
< 0.1%
33758 1
< 0.1%
32615 1
< 0.1%
32411 1
< 0.1%
32306 1
< 0.1%
31875 1
< 0.1%
30595 1
< 0.1%
29557 1
< 0.1%
29442 1
< 0.1%

percentage0to15years
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.328553
Minimum0
Maximum45
Zeros43
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size186.3 KiB
2024-07-05T11:49:19.573817image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q112
median15
Q318
95-th percentile24
Maximum45
Range45
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.1458331
Coefficient of variation (CV)0.33570246
Kurtosis1.5913793
Mean15.328553
Median Absolute Deviation (MAD)3
Skewness0.29033781
Sum182793
Variance26.479598
MonotonicityNot monotonic
2024-07-05T11:49:19.847049image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
16 1239
 
10.4%
15 1209
 
10.1%
17 1049
 
8.8%
14 1025
 
8.6%
13 905
 
7.6%
18 860
 
7.2%
12 709
 
5.9%
19 670
 
5.6%
11 586
 
4.9%
20 497
 
4.2%
Other values (34) 3176
26.6%
ValueCountFrequency (%)
0 43
 
0.4%
1 39
 
0.3%
2 51
 
0.4%
3 66
 
0.6%
4 86
 
0.7%
5 117
 
1.0%
6 131
1.1%
7 197
1.7%
8 250
2.1%
9 309
2.6%
ValueCountFrequency (%)
45 1
 
< 0.1%
42 1
 
< 0.1%
41 2
 
< 0.1%
40 1
 
< 0.1%
39 1
 
< 0.1%
38 3
 
< 0.1%
37 4
 
< 0.1%
36 3
 
< 0.1%
35 9
0.1%
34 14
0.1%

percentage15to25years
Real number (ℝ)

Distinct76
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.398826
Minimum0
Maximum96
Zeros18
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size186.3 KiB
2024-07-05T11:49:20.042750image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median12
Q314
95-th percentile19
Maximum96
Range96
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.2909256
Coefficient of variation (CV)0.42672795
Kurtosis39.289237
Mean12.398826
Median Absolute Deviation (MAD)2
Skewness4.5002
Sum147856
Variance27.993893
MonotonicityNot monotonic
2024-07-05T11:49:20.247109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 1705
14.3%
12 1542
12.9%
10 1527
12.8%
13 1223
10.3%
9 1040
8.7%
14 1005
8.4%
15 647
 
5.4%
8 614
 
5.1%
16 466
 
3.9%
7 354
 
3.0%
Other values (66) 1802
15.1%
ValueCountFrequency (%)
0 18
 
0.2%
1 12
 
0.1%
2 32
 
0.3%
3 28
 
0.2%
4 66
 
0.6%
5 127
 
1.1%
6 214
 
1.8%
7 354
 
3.0%
8 614
5.1%
9 1040
8.7%
ValueCountFrequency (%)
96 1
< 0.1%
81 1
< 0.1%
79 1
< 0.1%
75 1
< 0.1%
73 2
< 0.1%
72 1
< 0.1%
71 2
< 0.1%
70 2
< 0.1%
69 1
< 0.1%
67 1
< 0.1%

percentage25to45years
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.145325
Minimum0
Maximum94
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size186.3 KiB
2024-07-05T11:49:20.423836image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q117
median21
Q325
95-th percentile38
Maximum94
Range94
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.0573004
Coefficient of variation (CV)0.36383753
Kurtosis4.1689312
Mean22.145325
Median Absolute Deviation (MAD)4
Skewness1.4196211
Sum264083
Variance64.92009
MonotonicityNot monotonic
2024-07-05T11:49:20.624060image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 922
 
7.7%
21 867
 
7.3%
19 848
 
7.1%
22 821
 
6.9%
18 736
 
6.2%
17 709
 
5.9%
23 671
 
5.6%
16 574
 
4.8%
24 573
 
4.8%
25 489
 
4.1%
Other values (64) 4715
39.5%
ValueCountFrequency (%)
0 7
 
0.1%
1 2
 
< 0.1%
2 11
 
0.1%
3 14
 
0.1%
4 10
 
0.1%
5 18
 
0.2%
6 20
 
0.2%
7 35
0.3%
8 64
0.5%
9 81
0.7%
ValueCountFrequency (%)
94 1
 
< 0.1%
78 1
 
< 0.1%
76 1
 
< 0.1%
74 1
 
< 0.1%
71 2
< 0.1%
70 1
 
< 0.1%
68 4
< 0.1%
66 2
< 0.1%
65 2
< 0.1%
64 1
 
< 0.1%

percentage45to65years
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.354717
Minimum0
Maximum74
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size186.3 KiB
2024-07-05T11:49:20.855129image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q126
median30
Q335
95-th percentile41
Maximum74
Range74
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.7762284
Coefficient of variation (CV)0.22323478
Kurtosis1.6093473
Mean30.354717
Median Absolute Deviation (MAD)4
Skewness-0.32707738
Sum361980
Variance45.917272
MonotonicityNot monotonic
2024-07-05T11:49:21.056504image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 781
 
6.5%
29 773
 
6.5%
30 761
 
6.4%
28 754
 
6.3%
32 739
 
6.2%
33 713
 
6.0%
27 692
 
5.8%
34 648
 
5.4%
26 584
 
4.9%
36 547
 
4.6%
Other values (52) 4933
41.4%
ValueCountFrequency (%)
0 8
0.1%
1 9
0.1%
2 10
0.1%
3 7
0.1%
4 11
0.1%
5 4
 
< 0.1%
6 5
 
< 0.1%
7 8
0.1%
8 10
0.1%
9 14
0.1%
ValueCountFrequency (%)
74 1
 
< 0.1%
69 1
 
< 0.1%
60 1
 
< 0.1%
59 1
 
< 0.1%
58 1
 
< 0.1%
56 1
 
< 0.1%
55 2
 
< 0.1%
54 4
< 0.1%
53 3
< 0.1%
52 6
0.1%

percentage65yearsorolder
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.783732
Minimum0
Maximum100
Zeros39
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size186.3 KiB
2024-07-05T11:49:21.272981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q114
median19
Q324
95-th percentile36
Maximum100
Range100
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.2741938
Coefficient of variation (CV)0.46877879
Kurtosis7.3708955
Mean19.783732
Median Absolute Deviation (MAD)5
Skewness1.6254794
Sum235921
Variance86.010671
MonotonicityNot monotonic
2024-07-05T11:49:21.473408image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 734
 
6.2%
18 662
 
5.6%
20 660
 
5.5%
17 641
 
5.4%
21 638
 
5.4%
16 591
 
5.0%
22 571
 
4.8%
15 558
 
4.7%
14 510
 
4.3%
24 497
 
4.2%
Other values (79) 5863
49.2%
ValueCountFrequency (%)
0 39
 
0.3%
1 43
 
0.4%
2 53
 
0.4%
3 59
 
0.5%
4 90
 
0.8%
5 97
 
0.8%
6 129
1.1%
7 165
1.4%
8 211
1.8%
9 251
2.1%
ValueCountFrequency (%)
100 1
< 0.1%
99 1
< 0.1%
98 2
< 0.1%
95 1
< 0.1%
92 1
< 0.1%
90 1
< 0.1%
88 2
< 0.1%
87 1
< 0.1%
85 1
< 0.1%
83 1
< 0.1%

percentageonepersonhouseholds
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.832553
Minimum0
Maximum100
Zeros10
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size186.3 KiB
2024-07-05T11:49:21.672985image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q122
median29
Q339
95-th percentile61
Maximum100
Range100
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.738969
Coefficient of variation (CV)0.4630156
Kurtosis1.5821845
Mean31.832553
Median Absolute Deviation (MAD)8
Skewness1.1346225
Sum379603.2
Variance217.2372
MonotonicityNot monotonic
2024-07-05T11:49:21.873205image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 439
 
3.7%
23 439
 
3.7%
25 432
 
3.6%
26 429
 
3.6%
28 429
 
3.6%
29 417
 
3.5%
27 415
 
3.5%
21 406
 
3.4%
22 404
 
3.4%
20 376
 
3.2%
Other values (112) 7739
64.9%
ValueCountFrequency (%)
0 10
 
0.1%
2 3
 
< 0.1%
3 3
 
< 0.1%
4 11
 
0.1%
5 17
 
0.1%
6 20
 
0.2%
7 26
 
0.2%
8 36
0.3%
9 41
0.3%
10 72
0.6%
ValueCountFrequency (%)
100 2
 
< 0.1%
99 2
 
< 0.1%
98 6
0.1%
96 3
 
< 0.1%
95 5
< 0.1%
94 7
0.1%
93 8
0.1%
92 7
0.1%
91 7
0.1%
90 2
 
< 0.1%

percentagehouseholdswithoutchildren
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.279094
Minimum0
Maximum72
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size186.3 KiB
2024-07-05T11:49:22.120487image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q127
median32
Q337
95-th percentile46
Maximum72
Range72
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.4640707
Coefficient of variation (CV)0.26221525
Kurtosis0.96452322
Mean32.279094
Median Absolute Deviation (MAD)5
Skewness0.1504076
Sum384928.2
Variance71.640493
MonotonicityNot monotonic
2024-07-05T11:49:22.321799image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 711
 
6.0%
34 647
 
5.4%
35 635
 
5.3%
31 599
 
5.0%
32 596
 
5.0%
30 568
 
4.8%
36 556
 
4.7%
29 491
 
4.1%
38 484
 
4.1%
37 484
 
4.1%
Other values (82) 6154
51.6%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 7
0.1%
2 5
 
< 0.1%
3 3
 
< 0.1%
4 10
0.1%
5 8
0.1%
6 10
0.1%
7 13
0.1%
8 6
0.1%
9 10
0.1%
ValueCountFrequency (%)
72 1
 
< 0.1%
71 1
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
67 1
 
< 0.1%
66 4
< 0.1%
65 2
 
< 0.1%
64 4
< 0.1%
63 6
0.1%
62 8
0.1%

percentagehouseholdswithchildren
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.897006
Minimum0
Maximum83
Zeros25
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size186.3 KiB
2024-07-05T11:49:22.506611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q129
median37
Q343
95-th percentile55
Maximum83
Range83
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.869121
Coefficient of variation (CV)0.33064377
Kurtosis0.56094792
Mean35.897006
Median Absolute Deviation (MAD)7
Skewness-0.18506623
Sum428071.8
Variance140.87604
MonotonicityNot monotonic
2024-07-05T11:49:22.706705image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 514
 
4.3%
40 495
 
4.2%
37 489
 
4.1%
39 486
 
4.1%
36 478
 
4.0%
35 464
 
3.9%
41 438
 
3.7%
34 430
 
3.6%
32 407
 
3.4%
33 389
 
3.3%
Other values (88) 7335
61.5%
ValueCountFrequency (%)
0 25
0.2%
1 22
0.2%
2 21
0.2%
3 25
0.2%
4 18
 
0.2%
5 36
0.3%
6 39
0.3%
7 39
0.3%
8 39
0.3%
9 47
0.4%
ValueCountFrequency (%)
83 1
 
< 0.1%
80 1
 
< 0.1%
79 1
 
< 0.1%
77 2
 
< 0.1%
76 2
 
< 0.1%
75 3
< 0.1%
74 3
< 0.1%
73 5
< 0.1%
72 3
< 0.1%
71 7
0.1%

percentagewesternmigrationbackground
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7548008
Minimum0
Maximum86
Zeros105
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size186.3 KiB
2024-07-05T11:49:22.906167image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q311
95-th percentile20
Maximum86
Range86
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.2456495
Coefficient of variation (CV)0.7133971
Kurtosis15.732682
Mean8.7548008
Median Absolute Deviation (MAD)3
Skewness2.7133905
Sum104401
Variance39.008138
MonotonicityNot monotonic
2024-07-05T11:49:23.106220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 1057
 
8.9%
6 1031
 
8.6%
4 1016
 
8.5%
5 1005
 
8.4%
8 994
 
8.3%
9 931
 
7.8%
3 827
 
6.9%
10 807
 
6.8%
11 659
 
5.5%
2 539
 
4.5%
Other values (55) 3059
25.7%
ValueCountFrequency (%)
0 105
 
0.9%
1 281
 
2.4%
2 539
4.5%
3 827
6.9%
4 1016
8.5%
5 1005
8.4%
6 1031
8.6%
7 1057
8.9%
8 994
8.3%
9 931
7.8%
ValueCountFrequency (%)
86 1
< 0.1%
81 1
< 0.1%
80 1
< 0.1%
74 1
< 0.1%
66 1
< 0.1%
65 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%
62 1
< 0.1%
61 2
< 0.1%

percentagenonwesternmigrationbackground
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0351363
Minimum0
Maximum99
Zeros1420
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size186.3 KiB
2024-07-05T11:49:23.322984image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q310
95-th percentile32
Maximum99
Range99
Interquartile range (IQR)9

Descriptive statistics

Standard deviation11.473027
Coefficient of variation (CV)1.4278572
Kurtosis10.477032
Mean8.0351363
Median Absolute Deviation (MAD)3
Skewness2.8850709
Sum95819
Variance131.63035
MonotonicityNot monotonic
2024-07-05T11:49:23.539238image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1710
14.3%
2 1430
12.0%
0 1420
11.9%
3 1176
 
9.9%
4 932
 
7.8%
5 617
 
5.2%
6 518
 
4.3%
7 417
 
3.5%
8 339
 
2.8%
9 304
 
2.5%
Other values (78) 3062
25.7%
ValueCountFrequency (%)
0 1420
11.9%
1 1710
14.3%
2 1430
12.0%
3 1176
9.9%
4 932
7.8%
5 617
 
5.2%
6 518
 
4.3%
7 417
 
3.5%
8 339
 
2.8%
9 304
 
2.5%
ValueCountFrequency (%)
99 1
 
< 0.1%
95 1
 
< 0.1%
92 2
< 0.1%
88 1
 
< 0.1%
87 2
< 0.1%
83 3
< 0.1%
82 3
< 0.1%
80 2
< 0.1%
79 4
< 0.1%
78 4
< 0.1%

percentagemen
Real number (ℝ)

Distinct299
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.558239
Minimum25
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size186.3 KiB
2024-07-05T11:49:23.722444image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile45.8
Q148.8
median50.1
Q352
95-th percentile56.2
Maximum100
Range75
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation3.7565098
Coefficient of variation (CV)0.074300645
Kurtosis27.246387
Mean50.558239
Median Absolute Deviation (MAD)1.6
Skewness2.6011137
Sum602907
Variance14.111366
MonotonicityNot monotonic
2024-07-05T11:49:23.922855image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 923
 
7.7%
49.4 202
 
1.7%
49.2 196
 
1.6%
49.1 191
 
1.6%
50.6 188
 
1.6%
50.7 188
 
1.6%
49.5 187
 
1.6%
51.4 183
 
1.5%
49.6 180
 
1.5%
49 175
 
1.5%
Other values (289) 9312
78.1%
ValueCountFrequency (%)
25 1
 
< 0.1%
26.2 1
 
< 0.1%
26.5 1
 
< 0.1%
27.3 1
 
< 0.1%
29.4 1
 
< 0.1%
30 1
 
< 0.1%
30.8 1
 
< 0.1%
31.6 1
 
< 0.1%
31.9 1
 
< 0.1%
33.3 3
< 0.1%
ValueCountFrequency (%)
100 4
< 0.1%
96.6 1
 
< 0.1%
96 1
 
< 0.1%
92.3 1
 
< 0.1%
91.7 1
 
< 0.1%
90.9 1
 
< 0.1%
90.5 1
 
< 0.1%
90 1
 
< 0.1%
87.5 1
 
< 0.1%
86.5 1
 
< 0.1%

Interactions

2024-07-05T11:49:15.058627image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:54.538597image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:56.582224image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:58.564519image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:00.297646image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:02.020191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:03.845844image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:06.278656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:08.078158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:09.827800image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:11.443075image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:13.070357image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:15.208942image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:54.720856image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:56.731606image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:58.714247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:00.447671image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:02.213303image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:04.690288image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:06.428610image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:08.227929image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:09.979381image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:11.593168image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:13.220437image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:15.348546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:54.849738image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:56.848385image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:58.848160image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:00.570377image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:02.330624image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:04.829122image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:06.569193image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:08.369813image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:10.110633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:11.710386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:13.346448image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:15.475064image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:55.003934image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:56.987134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:58.981312image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:00.697622image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:02.479879image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:04.996259image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:06.763988image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:08.510863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:10.243568image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:11.843760image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:13.826080image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:15.608414image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:55.148817image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:57.120456image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:59.120532image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:00.880587image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:02.613628image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:05.153807image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:06.966170image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:08.644559image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:10.374465image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:11.969656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:13.959229image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:15.753798image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:55.316080image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:57.264878image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:59.263957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:01.030805image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:02.767782image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:05.312157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:07.111910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:08.794063image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:10.510960image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:12.103789image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:14.103591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:15.875989image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:55.515685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:57.414729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:59.381579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:01.165685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:02.913023image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:05.462535image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:07.245291image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:08.927726image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:10.627571image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:12.243499image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:14.237314image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:16.025111image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:55.682234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:57.548053image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:59.531280image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:01.297424image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:03.062710image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:05.595288image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:07.378418image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:09.103909image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:10.770957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:12.376396image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:14.359237image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:16.242691image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:55.839604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:57.698481image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:59.730459image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:01.446906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:03.279479image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:05.745822image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:07.511333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:09.261562image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:10.910837image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:12.510245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:14.509399image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:16.358566image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:56.003696image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:57.814631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:59.863917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:01.566403image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:03.413125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:05.878379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:07.644554image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:09.393576image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:11.036945image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:12.672321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:14.644958image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:16.491726image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:56.215647image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:57.953930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:59.997622image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:01.697095image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:03.568794image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:06.010703image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:07.772910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:09.527521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:11.154036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:12.792395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:14.775510image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:16.625167image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:56.432273image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:48:58.103794image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:00.147156image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:01.830465image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:03.712749image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:06.137311image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:07.927798image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:09.677148image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:11.299452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:12.909997image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:49:14.908540image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-07-05T11:49:24.087850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
percentage0to15yearspercentage15to25yearspercentage25to45yearspercentage45to65yearspercentage65yearsorolderpercentagehouseholdswithchildrenpercentagehouseholdswithoutchildrenpercentagemenpercentagenonwesternmigrationbackgroundpercentageonepersonhouseholdspercentagewesternmigrationbackgroundpopulationdensityperkm2
percentage0to15years1.0000.0360.270-0.224-0.4540.659-0.225-0.0030.053-0.363-0.2110.087
percentage15to25years0.0361.000-0.0320.113-0.4630.331-0.2260.257-0.048-0.166-0.165-0.105
percentage25to45years0.270-0.0321.000-0.599-0.534-0.097-0.597-0.0260.5690.3980.3400.531
percentage45to65years-0.2240.113-0.5991.0000.0360.2530.4900.323-0.491-0.461-0.269-0.492
percentage65yearsorolder-0.454-0.463-0.5340.0361.000-0.4540.480-0.302-0.1980.146-0.006-0.124
percentagehouseholdswithchildren0.6590.331-0.0970.253-0.4541.0000.0160.211-0.289-0.787-0.435-0.257
percentagehouseholdswithoutchildren-0.225-0.226-0.5970.4900.4800.0161.0000.127-0.544-0.533-0.347-0.479
percentagemen-0.0030.257-0.0260.323-0.3020.2110.1271.000-0.310-0.270-0.208-0.429
percentagenonwesternmigrationbackground0.053-0.0480.569-0.491-0.198-0.289-0.544-0.3101.0000.5360.5800.724
percentageonepersonhouseholds-0.363-0.1660.398-0.4610.146-0.787-0.533-0.2700.5361.0000.5360.482
percentagewesternmigrationbackground-0.211-0.1650.340-0.269-0.006-0.435-0.347-0.2080.5800.5361.0000.481
populationdensityperkm20.087-0.1050.531-0.492-0.124-0.257-0.479-0.4290.7240.4820.4811.000

Missing values

2024-07-05T11:49:16.847604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-05T11:49:17.244125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

neighborhoodcodeneighborhoodnamepopulationdensityperkm2percentage0to15yearspercentage15to25yearspercentage25to45yearspercentage45to65yearspercentage65yearsorolderpercentageonepersonhouseholdspercentagehouseholdswithoutchildrenpercentagehouseholdswithchildrenpercentagewesternmigrationbackgroundpercentagenonwesternmigrationbackgroundpercentagemen
0BU00030000Appingedam-Centrum2798.010.010.021.030.029.053.028.018.06.04.047.6
1BU00030001Appingedam-West1919.016.011.019.034.020.027.038.036.05.04.049.7
2BU00030002Appingedam-Oost2045.016.010.021.028.025.036.031.033.09.010.048.4
3BU00030007Verspreide huizen Damsterdiep en Eemskanaal61.022.011.022.034.010.018.029.053.07.02.053.0
4BU00030008Verspreide huizen ten zuiden van Eemskanaal18.015.010.011.048.017.020.044.037.03.01.055.0
5BU00030009Verspreide huizen ten noorden van het Damsterdiep21.020.013.014.034.019.025.038.037.08.02.050.0
6BU00050000Bedum2511.016.012.021.030.022.030.034.036.04.04.049.6
7BU00050001Verspreide huizen Bedum40.023.010.028.025.014.018.035.047.05.02.050.0
8BU00050002Zuidwolde1256.014.010.018.036.021.034.036.030.05.04.050.8
9BU00050003Verspreide huizen Zuidwolde18.019.010.015.035.022.021.040.040.05.03.052.0
neighborhoodcodeneighborhoodnamepopulationdensityperkm2percentage0to15yearspercentage15to25yearspercentage25to45yearspercentage45to65yearspercentage65yearsorolderpercentageonepersonhouseholdspercentagehouseholdswithoutchildrenpercentagehouseholdswithchildrenpercentagewesternmigrationbackgroundpercentagenonwesternmigrationbackgroundpercentagemen
13294BU19550108Verspreide huizen Beek106.014.012.021.033.019.024.031.045.07.01.050.7
13296BU19550200Didam-Zuid3259.013.012.018.033.025.031.036.032.07.05.048.8
13297BU19550201Didam-Noord4716.015.011.019.030.025.028.036.036.07.05.048.8
13298BU19550202Loil2880.014.014.019.037.016.024.033.042.04.02.052.4
13299BU19550203Nieuw-Dijk3055.015.014.018.038.015.022.028.050.04.01.051.2
13300BU19550205Verspreide huizen De Heegh238.020.018.020.034.08.016.021.063.02.00.051.3
13301BU19550206Verspreide huizen Greffelkamp69.018.09.022.032.020.020.034.046.03.01.052.1
13302BU19550207Verspreide huizen De Hogenend en Oud-Dijk212.017.010.034.027.011.028.030.042.06.02.053.2
13303BU19550208Verspreide huizen Nieuw-Dijk93.010.012.019.039.021.018.040.042.05.00.051.9
13304BU19550209Verspreide huizen Loil92.017.011.020.031.022.011.040.049.04.01.051.7